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Wyświetlanie 1-2 z 2
Tytuł:
Measuring and Testing Mutual Dependence of Multivariate Functional Data
Autorzy:
Krzyśko, Mirosław
Smaga, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/1058987.pdf
Data publikacji:
2020-09-04
Wydawca:
Główny Urząd Statystyczny
Tematy:
characteristic function
dependence measure
distance covariance
multivariate functional data
permutation method
test of independence
Opis:
This paper considers new measures of mutual dependence between multiple multivariate random processes representing multidimensional functional data. In the case of two processes, the extension of functional distance correlation is used by selecting appropriate weight function in the weighted distance between characteristic functions of joint and marginal distributions. For multiple random processes, two measures are sums of squared measures for pairwise dependence. The dependence measures are zero if and only if the random processes are mutually independent. This property is used to construct permutation tests for mutual independence of random processes. The finite sample properties of these tests are investigated in simulation studies. The use of the tests and the results of simulation studies are illustrated with an example based on real data.
Źródło:
Statistics in Transition new series; 2020, 21, 3; 21-37
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Duration-Based Approach to VaR Independence Backtesting
Autorzy:
Małecka, Marta
Powiązania:
https://bibliotekanauki.pl/articles/465936.pdf
Data publikacji:
2014
Wydawca:
Główny Urząd Statystyczny
Tematy:
VaR backtesting
Markov test, Haas test
TUFF test
Weibull test
gamma test
EACD test
Opis:
Dynamic development in the area of value-at-risk (VaR) estimation and growing implementation of VaR-based risk valuation models in investment companies stimulate the need for statistical methods of VaR models evaluation. Following recent changes in Basel Accords, current UE banking supervisory regulations require internal VaR model backtesting, which provides another strong incentive for research on relevant statistical tests. Previous studies have shown that commonly used VaR independence Markov-chain-based testing procedure exhibits low power, which constitutes a particularly serious problem in the case of finite-sample settings. In the paper, as an alternative to the popular Markov test an overview of the group of duration-based VaR backtesting procedures is presented along with exploration of their statistical properties while rejecting a non-realistic assumption of infinite sample size. The Monte Carlo test technique was adopted to provide exact tests, in which asymptotic distributions were replaced with simulated finite sample distributions. A Monte Carlo study, based on the GARCH model, was designed to investigate the size and the power of the tests. Through the comparative analysis we found that, in the light of observed statistical properties, the duration-based approach was superior to the Markov test.
Źródło:
Statistics in Transition new series; 2014, 15, 4; 627-636
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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